CN112052598B

CN112052598B - Satellite ground station resource multi-objective optimization method based on preference MOEA

Info

Publication number: CN112052598B
Application number: CN202010959510.7A
Authority: CN
Inventors: 陈浩; 孙刚; 伍江江; 彭双; 杜春; 吴晔; 李军; 熊伟; 陈荦; 钟志农; 欧阳雪; 杨岸然; 景宁
Original assignee: National University of Defense Technology
Current assignee: National University of Defense Technology
Priority date: 2020-09-14
Filing date: 2020-09-14
Publication date: 2024-06-21
Anticipated expiration: 2040-09-14
Also published as: CN112052598A

Abstract

The application relates to a satellite ground station resource multi-objective optimization method based on preference MOEA, which comprises the following steps: and acquiring an available resource set of the satellite ground station to the task satellite, and constructing a task resource allocation scheme individual according to a preset coding rule. Updating the current individual based on a multi-objective evolutionary algorithm and a heuristic strategy, so that the updated individual sequentially performs resource occupation amount expansion matching, resource occupation conflict resolution and resource occupation amount reduction according to preset task resource demand data, and obtains an optimized individual according to preset task resource allocation preference data. And when the preset iteration update termination condition is met, obtaining an optimized task resource allocation scheme of the satellite ground station according to the optimized individual. According to the method, constraint conditions in satellite ground station resource allocation are processed by heuristic strategies, and the algorithm process is guided by task resource allocation preference, so that a task allocation scheme which is reasonable and feasible and accords with task preference can be directly given, and the pertinence of the scheme is enhanced.

Description

Satellite ground station resource multi-objective optimization method based on preference MOEA

Technical Field

The application relates to the technical field of satellite system resource planning, in particular to a satellite ground station resource multi-objective optimization method based on preference MOEA.

Background

The current research direction of satellite ground station resource planning problems is mainly focused on single-target optimization, and the research in the multi-target optimization direction is relatively less. Because the satellite ground station resource planning problem is a multi-objective optimization problem in nature, that is, the optimization objective focused by the satellite management mechanism is often more than one, the single-objective optimization method cannot meet the actual needs.

The disclosed satellite ground station resource planning Multi-objective optimization technology is mainly based on MOEA (Multi-Objective Evolutionary Algorithm ), a series of solutions which are compromised on the optimization objective are obtained through the MOEA, and a set of solutions which meet expectations are selected from the solutions according to actual needs by a satellite management mechanism. The multi-objective optimization method for satellite ground station resource planning cuts off the optimization process and the decision process in the processing mode of the problem, can not directly obtain solutions meeting the expectations of a satellite management mechanism, and has poor solving pertinence.

Disclosure of Invention

Based on this, it is necessary to provide a method for optimizing satellite ground station resources based on preference MOEA, which can directly give solutions meeting the expectations of satellite authorities by combining an "optimization process" with a "decision process", and the method comprises:

And acquiring an available resource set of the satellite ground station to the task satellite, and constructing an individual corresponding to the task resource allocation scheme according to a preset coding rule and the available resource set.

Updating a current individual based on a multi-target evolutionary algorithm, modifying the updated individual based on a heuristic strategy, enabling a task resource allocation scheme corresponding to the updated individual to sequentially perform resource occupation amount expansion matching, resource occupation conflict resolution and resource occupation amount reduction according to preset task resource demand data, and acquiring an optimized individual from the updated individual according to preset task resource allocation preference data.

And when the preset iteration update termination condition is met, obtaining an optimized task resource allocation scheme of the satellite ground station according to the optimized individual.

In one embodiment, the step of obtaining the set of available resources of the satellite ground station for the task satellite, and constructing an individual corresponding to the task resource allocation scheme according to the preset coding rule and the set of available resources includes:

a set of visible time windows of a ground antenna of a satellite ground station for a mission satellite is obtained.

And constructing an individual corresponding to the task resource allocation scheme by using a preset coding rule according to the occupied state of the visible time window.

In one embodiment, updating a current individual based on a multi-objective evolutionary algorithm, and modifying the updated individual based on a heuristic strategy, so that a task resource allocation scheme corresponding to the updated individual sequentially performs resource occupation amount expansion matching, resource occupation conflict resolution and resource occupation amount reduction according to preset task resource demand data, and the step of obtaining an optimized individual from the updated individual according to preset task resource allocation preference data comprises the following steps:

The current individual is updated based on the multi-objective evolutionary algorithm.

And modifying the updated individual according to a preset task expansion strategy, and increasing the occupation quantity of the visible time windows of the task resource allocation scheme corresponding to the updated individual according to preset task resource demand data to obtain the task expansion individual.

And resolving the resource occupation conflict in the task resource allocation scheme corresponding to the task expansion individual according to the preset conflict resolution strategy and the preset task resource demand data to obtain a conflict resolution individual. The resource occupancy conflict includes: ground antenna occupancy conflicts and mission satellite occupancy conflicts.

The occupied visible time window in the task resource allocation scheme corresponding to the conflict resolution individual is obtained, and the occupied visible time window is shortened according to preset task resource demand data and task reduction strategies, so that the task reduction individual is obtained.

And acquiring an optimized individual from the task reduction individuals according to preset task resource allocation preference data.

In one embodiment, the step of modifying the updated individual according to a preset task expansion policy, and increasing the occupation number of the visible time windows of the task resource allocation scheme corresponding to the updated individual according to preset task resource demand data, to obtain the task expansion individual includes:

and acquiring the occupation quantity of the visible time windows corresponding to the task satellites in the task resource allocation scheme corresponding to the updated individuals.

When the occupation quantity of the visible time windows corresponding to the task satellites is smaller than a preset value, the occupation quantity of the visible time windows of the task satellites in the task resource allocation scheme is increased according to a preset expansion coefficient, and a task expansion individual is obtained.

In one embodiment, according to a preset conflict resolution policy, the step of resolving a resource occupation conflict in a task resource allocation scheme corresponding to a task expansion individual according to preset task resource demand data to obtain a conflict resolution individual includes:

the occupied visible time window corresponding to each ground antenna in the task resource allocation scheme corresponding to the task expansion individual is obtained, and the ground antenna resource occupation conflict is resolved according to the preset antenna task duration and antenna task period conflict resolution rules.

The occupied visible time window corresponding to each task satellite in the task resource allocation scheme after the ground antenna resource occupation conflict is resolved is obtained, task satellite resource occupation conflict is resolved according to the preset satellite task duration time and satellite task time period conflict resolution rules, and a conflict resolution individual is obtained.

In one embodiment, the step of obtaining the task reduction individual includes:

and acquiring an occupied visible time window corresponding to the ground antenna in the task resource allocation scheme corresponding to the conflict resolution individual, and reserving the time period of the occupied visible time window corresponding to the ground antenna within a preset task time period to obtain the time period reduction individual.

The method comprises the steps of obtaining occupied visible time windows corresponding to task satellites in a task resource allocation scheme corresponding to a time period reduction individual, canceling the occupied visible time windows according to preset task satellite demand times, enabling the occupied number of the visible time windows corresponding to the task satellites to be equal to the preset task satellite demand times, and obtaining the task reduction individual.

And modifying the updated individual based on the heuristic strategy, so that the task resource allocation scheme corresponding to the updated individual sequentially performs resource occupation amount expansion matching, resource occupation conflict resolution and resource occupation amount reduction according to the preset task resource demand data.

And calculating the fitness value of the updated individual according to a preset optimization objective function. The fitness value comprises a task planning failure rate of an individual corresponding task resource allocation scheme and a ground antenna load balance value.

And acquiring an optimized individual from the updated individual according to the preset task resource allocation preference data and the individual fitness value. The task resource allocation preference data includes: a mission planning failure rate preference value and a ground antenna load balance preference value.

A satellite ground station resource multi-objective optimization apparatus based on preference MOEA, the apparatus comprising:

The individual construction module is used for acquiring an available resource set of the satellite ground station to the task satellite, and constructing an individual corresponding to the task resource allocation scheme according to a preset coding rule and the available resource set.

And the individual iteration updating module is used for updating the current individual based on the multi-objective evolutionary algorithm, modifying the updated individual based on the heuristic strategy, enabling the task resource allocation scheme corresponding to the updated individual to sequentially perform resource occupation amount expansion matching, resource occupation conflict resolution and resource occupation amount reduction according to preset task resource demand data, and acquiring the optimized individual from the updated individual according to preset task resource allocation preference data.

And the task resource allocation scheme output module is used for obtaining an optimized task resource allocation scheme of the satellite ground station according to the optimized individual when the preset iteration update termination condition is met.

A computer device comprising a memory storing a computer program and a processor which when executing the computer program performs the steps of:

A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:

According to the method, the device, the computer equipment and the storage medium for optimizing the satellite ground station resources based on the preference MOEA, an individual corresponding to a task resource allocation scheme is constructed according to the available resource set of the satellite ground station to the task satellite; in the process of carrying out iterative updating on an individual based on preference MOEA, carrying out resource occupation amount expansion matching and resource occupation conflict resolution and resource occupation amount reduction on a task resource allocation scheme corresponding to the individual based on a heuristic strategy according to preset task resource demand data in sequence so as to process a plurality of constraint conditions in satellite ground station resource planning, ensure the rationality and feasibility of a task planning result and gradually limit the search space of the preference MOEA within a high-quality solution space; when an optimized individual is selected, the process of an algorithm can be guided according to the requirements of different types of tasks according to preset task resource allocation preference data, and the combination of an optimization process and a decision process is embodied. The application can directly provide a satellite ground station resource allocation scheme which accords with the expectations of a satellite management mechanism, enhances the pertinence of task planning of the satellite ground station and improves the resource utilization efficiency.

Drawings

FIG. 1 is an application scenario diagram of a preferred MOEA-based satellite ground station resource multi-objective optimization method in one embodiment;

FIG. 2 is a schematic diagram illustrating steps of a method for optimizing satellite ground station resources based on a preferred MOEA, according to one embodiment;

FIG. 3 is a flow chart of a method for optimizing satellite ground station resources based on a preferred MOEA in one embodiment;

FIG. 4 is a schematic diagram of a solution space definition process of a preferred MOEA-based satellite ground station resource multi-objective optimization method in another embodiment;

FIG. 5 is a schematic diagram of an expansion strategy based on ground antenna load balancing in one embodiment;

FIG. 6 is a diagram of a task duration sliding rule in a conflict resolution policy in one embodiment;

fig. 7 is an internal structural diagram of a computer device in one embodiment.

Detailed Description

The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

The satellite ground station resource multi-objective optimization method based on the preference MOEA can be applied to an application environment shown in fig. 1. The task resource allocation device 102 communicates with the task resource management device 104 through a network, and obtains currently available satellite ground station resources, including a ground antenna and parameters thereof, task satellite parameters and the like, from the task resource control device 104 according to input task data to be executed, and designates a calling mode of the satellite ground antenna to each task satellite. The implementation of the task resource allocation device 102 and the task resource control device 104 may be, but is not limited to, various servers, personal computers, notebook computers, or combinations thereof.

In one embodiment, as shown in fig. 2, a method for optimizing satellite ground station resources based on preference MOEA is provided, and the method is applied to the task resource allocation device 102 in fig. 1 for illustration, and includes the following steps:

Step 202, acquiring an available resource set of a satellite ground station for a task satellite, and constructing an individual corresponding to a task resource allocation scheme according to a preset coding rule and the available resource set.

The available resources of the satellite ground station are mainly ground antennas, and according to the idle time periods of each ground antenna and the task satellite and the relative position relation (including antenna orientation) of the ground antennas and the task satellite, the possible time periods of data transmission between the satellite ground station and the task satellite through a certain ground antenna can be known. The application takes these possible time periods and their corresponding ground antennas and mission satellites as the set of available resources of the satellite ground station to the mission satellites. Note that, the task satellite herein refers to a satellite that may be called by a task to be performed, and may be all satellites in a satellite system, or may be a part of satellites specified according to task data to be performed.

For the available resource set, an initial task resource allocation scheme is generated by adopting a preset coding rule and is used as an individual in the preference MOEA. The encoding rules include: the occupied status of each resource in the set of available resources is identified by encoding, and an initial set of task resource allocation schemes is generated by encoding.

Step 204, updating the current individual based on the multi-objective evolutionary algorithm, modifying the updated individual based on the heuristic strategy, enabling the task resource allocation scheme corresponding to the updated individual to sequentially perform resource occupation amount expansion matching, resource occupation conflict resolution and resource occupation amount reduction according to preset task resource demand data, and acquiring the optimized individual from the updated individual according to preset task resource allocation preference data.

The existing algorithm flow of preference MOEA mainly comprises operations such as initialization, selection, crossover, variation, individual fitness calculation, construction of next generation population by combining preference information, termination condition judgment and the like. According to the application, algorithms are taken as an ontology, and heuristic strategies based on domain knowledge are embedded to ensure the rationality and feasibility of task resource allocation.

Upon completion of the initialization in step 202, the initialized individuals are first subjected to modification based on the multi-objective evolutionary algorithm, such as selection, crossover, mutation, etc. And then modifying the individual based on heuristic strategies according to the task resource demand data: the resource occupation amount of the task resource allocation scheme corresponding to the individual is increased through the task expansion strategy, so that the resource occupation amount of each task satellite and each ground antenna is not less than the requirement of task resource demand data, and the possibility of task resource allocation failure caused by antenna uniqueness constraint, satellite uniqueness constraint, task non-conflict constraint and the like in the individual is reduced; the resource occupation conflict in the task resource allocation scheme corresponding to the individual is eliminated through a conflict resolution strategy, wherein the resource occupation conflict comprises task satellite occupation conflict, ground antenna occupation conflict and the like; according to the requirements in task resource allocation demand data of a task to be executed, the resource occupation amount of an individual corresponding task resource allocation scheme is reduced through a task reduction strategy, so that resources occupied in the task resource allocation scheme just meet the task resource demand, and balance of resource use in the whole satellite system is considered (the balance consideration includes equipment working states such as equipment load amount and working time, and constraints and limits of equipment service life, equipment failure rate and the like on the working states). And finally, calculating corresponding individual fitness values according to the task resource allocation preference data, and selecting optimized individuals according to the individual fitness values to construct a next generation population. According to requirements of task types, priority levels and the like, task resource allocation preference data can be one or more items, and then calculation of individual fitness values is also one or more items corresponding to the calculation; and when the optimized individual is selected, selecting according to the closeness degree of the fitness value of the individual and the task resource allocation preference data.

And 206, obtaining an optimized task resource allocation scheme of the satellite ground station according to the optimized individual when the preset iteration update termination condition is met.

According to the application, a preference MOEA algorithm is taken as a body, a heuristic strategy based on domain knowledge is embedded, a plurality of constraint conditions in satellite ground station resource planning can be processed, and the rationality and feasibility of a task planning result are ensured; when an optimized individual is selected, the process of a demand guide algorithm based on different types of tasks can be realized according to preset task resource allocation preference data, the combination of an optimization process and a decision process is realized, a satellite ground station resource allocation scheme meeting the expectations of a satellite management mechanism can be directly given, the pertinence of task planning of a satellite ground station is enhanced, and the resource utilization efficiency is improved.

In one embodiment, as shown in fig. 3, a method for optimizing satellite ground station resources based on preference MOEA is provided, which includes the following steps:

step 302, a set of visible time windows for a mission satellite by a ground antenna of a satellite ground station is obtained. And constructing an individual corresponding to the task resource allocation scheme by using a preset coding rule according to the occupied state of the visible time window.

The coding rule adopted in this embodiment is: the random bits are binary encoded. Wherein each binary bit corresponds to a visible time window of a satellite ground station relative to a mission satellite, when the value of the binary bit is taken as '1', the visible time window is occupied (i.e. a mission is planned in the visible time window) in the mission resource allocation scheme, and when the value of the binary bit is taken as '0', the visible time window is unoccupied (i.e. no mission is planned in the visible time window).

Step 304, updating the current individual based on the multi-objective evolutionary algorithm.

In this embodiment, the updating operation of the individual by the multi-objective evolutionary algorithm includes: selection operation, crossover operation and mutation operation. Wherein the selecting is performed using binary tournament selection (Binary Tournament Select), i.e., selecting two individuals randomly in the current population, comparing the values of the predefined optimization objective functions of the two individuals, and selecting the individual closer to the optimization objective as a parent. The crossover probability of the crossover operation on the individual in this embodiment is set to 0.7. Wherein the interleaving is performed using HUX (Half Uniform Crossover, semi-uniform interleaving) and a pair of individually encoded distinct bit values of the interleaving is exchanged with a probability of one half. The mutation operation is performed by BF (Bit Flip), the inversion operation is performed with a certain probability on each binary Bit of the individual code, if a certain binary Bit value is "1", the value becomes "0" after the mutation operation is performed, and similarly, if "0" becomes "1", the mutation probability is set to 0.01 in this embodiment.

Step 306, obtaining the occupation quantity of the visible time windows corresponding to the task satellites in the task resource allocation scheme corresponding to the updated individuals. When the occupation quantity of the visible time windows corresponding to the task satellites is smaller than a preset value, the occupation quantity of the visible time windows of the task satellites in the task resource allocation scheme is increased according to a preset expansion coefficient, and a task expansion individual is obtained.

The present embodiment embeds three serial domain knowledge based heuristic strategies, namely a task expansion strategy, a conflict resolution strategy and a task reduction strategy, between the "mutation" and "individual fitness calculation" operations of the preference MOEA section, gradually restricting the search space of the preference MOEA within the solution space with good quality (as shown in fig. 4).

Specifically, in the task resource allocation scheme corresponding to the individual updated by the multi-objective evolutionary algorithm, two typical cases may occur: (1) The number of occupied visible time windows is smaller than the task resource requirement, if the task resource requirement data require a certain satellite to execute 2 tasks, but only 1 or 0 occupied visible time windows corresponding to the satellite in the task resource allocation scheme corresponding to the individual are needed; (2) The task resource allocation scheme corresponding to the individual may violate constraint conditions, such as ground antenna uniqueness constraint, satellite uniqueness constraint, task non-conflict constraint and the like, in the task planning process. The reason for the failure of task resource allocation is partly due to feature (1) and partly due to the subsequent conflict resolution operation for feature (2).

The task extension strategy enables the feature (1) to be absent by increasing the number of visible time windows occupied by the resource allocation scheme, and can provide a certain redundancy to reduce the influence of the feature (2) on the task planning failure rate. The present embodiment employs the following task extension policies: when the occupation number of the visible time windows corresponding to a certain satellite in the task resource allocation scheme corresponding to an individual is smaller than the task satellite demand times corresponding to the task resource demand data (the difference value is assumed to be n), n visible time windows corresponding to the satellite are increased and occupied in the task resource allocation scheme according to a preset expansion rule.

Step 308, obtaining an occupied visible time window corresponding to each ground antenna in a task resource allocation scheme corresponding to the task expansion individual, and resolving the ground antenna resource occupation conflict according to a preset antenna task duration and antenna task period conflict resolution rule. The occupied visible time window corresponding to each task satellite in the task resource allocation scheme after the ground antenna resource occupation conflict is resolved is obtained, task satellite resource occupation conflict is resolved according to the preset satellite task duration time and satellite task time period conflict resolution rules, and a conflict resolution individual is obtained.

Specifically, in this embodiment, the ground antenna conflict resolution is performed first, and then the satellite conflict resolution is performed, so that the task resource allocation scheme corresponding to the individual meets the constraint condition. The two kinds of conflict resolution can be the same processing method, and the ground antenna conflict resolution is taken as an example for illustration.

In the ground antenna conflict resolution, the occupied visible time window is divided into a plurality of subsets based on the ground antenna ID, the number of the subsets is consistent with that of the ground antennas, and the conflict resolution is carried out for each subset. The idea of sliding a window is adopted when conflict resolution is performed, namely, the starting time and the ending time of the task duration are slid in the corresponding visible time window. In the initial individual, the start time and the end time of the task duration are set as the start time and the end time of the visible time window, respectively. According to the antenna task duration of each ground antenna, on the premise of meeting the minimum required duration of the task, a start time sliding interval and an end time sliding interval of the task are determined in a visible time window, as shown in fig. 5.

In fig. 5, the start time and the end time of the visible time window w are w_st and w_et, respectively, and the antenna task duration within the visible time window is set to w_st and w_et, respectively, at the time of initialization. Let t _min be the shortest antenna-task duration, i.e., the antenna-task duration cannot be shorter than t _min, so the latest start time of the task cannot exceed r_latest_st (r_latest_st=w_et-t _min), the earliest end time of the task cannot be earlier than r_ earliest _et (r_ earliest _et=w_st+t _min), i.e., the start time sliding interval of the antenna-task duration is between w_st and r_latest_st, and the end time sliding interval is between r_ earliest _et and w_et.

When conflict resolution is carried out on a subset corresponding to a certain antenna, the occupied visible time windows (namely the antenna task duration time periods) corresponding to the subset are arranged in an ascending order according to the starting time, any two antenna task duration time periods in the subset are compared, and the fact that any two antenna task duration time periods are compared only once is ensured. When comparing, if the two are coincident, modifying the starting time and the ending time of the corresponding antenna task duration according to the sliding range; if the constraint condition cannot be met through sliding, deleting one of the antenna task duration periods (namely canceling the occupation of the corresponding visible time window) according to a preset deletion rule.

Step 310, obtaining an occupied visible time window corresponding to a ground antenna in a task resource allocation scheme corresponding to the conflict resolution individual, and reserving a time period of the occupied visible time window corresponding to the ground antenna within a preset task time period to obtain a time period reduction individual. The method comprises the steps of obtaining occupied visible time windows corresponding to task satellites in a task resource allocation scheme corresponding to a time period reduction individual, canceling the occupied visible time windows according to preset task satellite demand times, enabling the occupied number of the visible time windows corresponding to the task satellites to be equal to the preset task satellite demand times, and obtaining the task reduction individual.

In the task resource allocation scheme corresponding to the conflict resolution individual, the total occupied time of part of ground antennas and/or task satellites may be greater than the task resource requirement, and the number of times of executing tasks may be greater than the task resource requirement, which may cause waste of satellite ground station resources.

The task reduction strategy adopted by the embodiment cuts off the part exceeding the total task time period in the task resource allocation scheme based on the starting and stopping of the total task; for the task satellites with the execution times of the task satellites being more than the demand times of the task satellites in the task resource allocation scheme, part of the visible time windows occupied by the task satellites are canceled according to a preset rule, so that the occupation quantity of the visible time windows corresponding to the task satellites is equal to the demand times of the task satellites required by the task resource demands.

Step 312, calculating the fitness value of the updated individual according to the preset optimization objective function. The fitness value comprises a task planning failure rate of an individual corresponding task resource allocation scheme and a ground antenna load balance value. And acquiring an optimized individual from the updated individual according to the preset task resource allocation preference data and the individual fitness value. The task resource allocation preference data includes: a mission planning failure rate preference value and a ground antenna load balance preference value.

In order to integrate the decision process into the optimization process, the embodiment guides the evolution direction of the algorithm according to the task resource allocation preference data and selects the optimized individuals according to the individual fitness value. The objective function of the individual comprises a task planning failure rate and ground antenna load balance degree, and the minimum objective function value is taken as an optimization target. The task planning failure rate is defined as the percentage of the number of tasks which cannot be scheduled to the total number of tasks, and the ground antenna load balance is defined as the ratio of the standard deviation of the working time length of the antenna participating in task planning in the planning period to the average working time length of the antenna.

The task resource allocation preference data comprises a task planning failure rate preference value and a ground antenna load balance preference value, and can be determined through a historical planning scheme or actual application requirements of a satellite management mechanism, and preferences are expressed in a mode of preference points in an algorithm. For example, in daily satellite ground station resource planning, a satellite management mechanism may select a relatively compromised "preference point" to compromise the task planning failure rate and the antenna load balance, thereby prolonging the service life of the device; when performing a major warranty task, satellite authorities may be more concerned with low failure rates in task planning to maximize the completion of the task.

And step 314, obtaining an optimized task resource allocation scheme of the satellite ground station according to the optimized individual when the preset iteration update termination condition is met.

In this embodiment, the algorithm running time is taken as a termination condition.

The embodiment provides a specific implementation mode of a satellite ground station resource multi-objective optimization method based on preference MOEA, constructs a heuristic strategy based on domain knowledge, combines the heuristic strategy with a preference MOEA algorithm to solve a multi-objective optimization problem of satellite ground station resource planning, and can directly solve part of the solution focused by a satellite management mechanism. Modeling a satellite ground station resource planning problem as a preference multi-objective optimization problem, abstracting domain knowledge as preference information setting, and further improving pertinence of problem solving; the heuristic strategy based on domain knowledge is provided and can be effectively combined with a preference MOEA algorithm, so that the optimality of problem solving is further improved.

In one embodiment, the updated individual is modified based on the heuristic strategy, so that the task resource allocation scheme corresponding to the updated individual performs resource occupation amount expansion matching according to preset task resource demand data, and the algorithm pseudo code of the used task expansion strategy is as follows:

Specifically, when the amount of occupied resources in the task resource allocation scheme is smaller than the requirements in the task resource demand data, the occupied resources in the task resource allocation scheme need to be increased according to the task expansion policy. The expansion modes adopted by the adopted task expansion strategy comprise a random expansion mode and a load balance-based expansion mode, in the embodiment, one half of individuals are subjected to the random expansion mode, and the other half of individuals are subjected to the load balance-based expansion mode. The random expansion mode refers to randomly occupying an unoccupied visible time window, so that the searching capability of an algorithm is enhanced. The expansion mode based on load balance is inspired by the load balance degree of each ground antenna at present, and the visible time window corresponding to the ground antenna with the minimum working time length is selected for expansion in each expansion process, so that the load balance degree of the ground antenna is improved. As shown in fig. 6, taking the satellite s ₁ as an example to illustrate the expansion mode based on load balancing, assuming that only 1 of occupied visible time windows corresponds to the satellite s ₁ (represented by the solid frame diamonds in the left column of the visible time window set), but the number of times of task resource requirements for executing tasks of the satellite s ₁ is 2, the working time lengths of all the unoccupied visible time windows (represented by the solid frame diamonds in the right column of the visible time window set) of the satellite s ₁, namely the working time lengths of the ground antennas a ₁～a₅, are calculated, wherein the working time length of the ground antennas a ₅ is the shortest, and the corresponding visible time window (represented by the dotted arrows) is set to be occupied.

The task expansion strategy provided in the embodiment can meet the task resource requirement, simultaneously give consideration to the searching performance of the algorithm and the load balance degree of the equipment, and can provide a more optimized ground station task resource allocation scheme.

In one embodiment, the updated individual is modified based on the heuristic strategy, so that the task resource allocation scheme corresponding to the updated individual performs resource occupation conflict resolution according to preset task resource demand data, and the algorithm pseudo code of the used conflict resolution strategy is as follows:

In this embodiment, the occupied visible time window is divided into a plurality of subsets based on the ground antenna ID, the number of the subsets is identical to the number of the ground antennas, and the duration of the antenna task in one subset is used as a task to resolve the conflict of each subset. The method comprises the following specific steps:

step 1: and sequentially selecting one task as a current task, and determining a starting time sliding interval and an ending time sliding interval of the current task.

Step 2: and sequentially selecting another task in the set as a comparison object (any two tasks are compared only once), and determining a start time sliding interval and an end time sliding interval of the tasks.

Step 3: comparing the two tasks in the step1 and the step 2, and enabling the two tasks to meet the constraint condition by sliding the starting time and the ending time of the two tasks.

Step 4: if the step 3 can not meet the constraint conditions, deleting one task of the two tasks according to the deleting rule and updating the set; otherwise, turning to step 2.

Step 5: if the current task is deleted, turning to step 1; if the comparison object is deleted, go to step 2.

Step 6: if step 2 traverses all tasks except the current task, step 1 is shifted.

Step 7: repeating the steps until all tasks in the set are traversed.

The deletion rule of this embodiment is: three deletion modes are adopted, and one third of individuals are deleted respectively. The three deletion modes comprise: the opportunity priority deleting mode refers to deleting tasks with more opportunities in the two modes, and reserving tasks with fewer opportunities, wherein the opportunities refer to the occupation number of visible time windows of corresponding task satellites. Random deletion refers to one of the two. The combined deletion rule refers to that when conflict resolution is carried out on the ground antenna, an opportunity priority deletion rule is adopted for one half of individuals, and a random deletion rule is adopted for the other half.

By adopting the conflict elimination strategy, the task planning failure rate can be reduced, and meanwhile, the limitation caused by a single strategy can be avoided by defining diversified deletion rules.

In one embodiment, modifying the updated individual based on the heuristic policy, so that the task resource allocation scheme corresponding to the updated individual performs the task reduction policy used for reducing the resource occupation amount according to the preset task resource demand data, where the task reduction policy includes:

And (3) based on a task reduction rule of load balancing, namely for a task satellite with the occupation quantity of the visible time window being larger than the task satellite demand times in the task resource demand data demand, calculating the working time of each ground antenna in the occupied visible time window corresponding to the task satellite, selecting the ground antenna with the largest working time, and canceling the occupation of the visible time window of the satellite on the ground antenna until the task satellite demand times demand of the task satellite is met.

According to the embodiment, the task reduction strategy is defined, so that the load balance of the task resource allocation scheme corresponding to the optimized individual can be further improved.

It should be understood that, although the steps in the flowchart of fig. 2 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 2 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.

In one embodiment, there is provided a satellite ground station resource multi-objective optimization apparatus based on preference MOEA comprising:

And the individual iteration updating module is used for updating the current individual based on the multi-objective evolutionary algorithm, modifying the updated individual based on the heuristic strategy, enabling the task resource allocation scheme corresponding to the updated individual to sequentially perform resource occupation amount expansion matching, resource occupation conflict resolution and resource occupation amount reduction, and acquiring the optimized individual from the updated individual according to the preset task resource allocation preference data.

In one embodiment, the individual construction module is configured to obtain a set of visible time windows of the ground antenna of the satellite ground station for the mission satellite. And constructing an individual corresponding to the task resource allocation scheme by using a preset coding rule according to the occupied state of the visible time window.

In one embodiment, the individual iteration update module is configured to update the current individual based on a multi-objective evolutionary algorithm. And modifying the updated individual according to a preset task expansion strategy, and increasing the occupation quantity of the visible time windows of the task resource allocation scheme corresponding to the updated individual to obtain the task expansion individual. And according to a preset conflict resolution strategy, resolving the resource occupation conflict in the task resource allocation scheme corresponding to the task expansion individual to obtain a conflict resolution individual. The resource occupancy conflict includes: ground antenna occupancy conflicts and mission satellite occupancy conflicts. The occupied visible time window in the task resource allocation scheme corresponding to the conflict resolution individual is obtained, and the occupied visible time window is shortened according to preset task resource demand data and task reduction strategies, so that the task reduction individual is obtained. And acquiring an optimized individual from the task reduction individuals according to preset task resource allocation preference data.

In one embodiment, the individual iteration updating module is configured to obtain the occupation number of the visible time window corresponding to the task satellite in the task resource allocation scheme corresponding to the updated individual. When the occupation quantity of the visible time windows corresponding to the task satellites is smaller than a preset value, the occupation quantity of the visible time windows of the task satellites in the task resource allocation scheme is increased according to a preset expansion coefficient, and a task expansion individual is obtained.

In one embodiment, the individual iteration updating module is configured to obtain an occupied visible time window corresponding to each ground antenna in a task resource allocation scheme corresponding to the task expansion individual, and resolve a ground antenna resource occupation conflict according to a preset antenna task duration and an antenna task period conflict resolution rule. The occupied visible time window corresponding to each task satellite in the task resource allocation scheme after the ground antenna resource occupation conflict is resolved is obtained, task satellite resource occupation conflict is resolved according to the preset satellite task duration time and satellite task time period conflict resolution rules, and a conflict resolution individual is obtained.

In one embodiment, the individual iteration updating module is configured to obtain an occupied visible time window corresponding to a ground antenna in a task resource allocation scheme corresponding to the conflict resolution individual, and reserve a period of the occupied visible time window corresponding to the ground antenna within a preset task period, so as to obtain a period reduction individual. The method comprises the steps of obtaining occupied visible time windows corresponding to task satellites in a task resource allocation scheme corresponding to a time period reduction individual, canceling the occupied visible time windows according to preset task satellite demand times, enabling the occupied number of the visible time windows corresponding to the task satellites to be equal to the task satellite demand times, and obtaining the task reduction individual.

In one embodiment, the individual iteration update module is configured to update the current individual based on a multi-objective evolutionary algorithm. And modifying the updated individual based on the heuristic strategy, so that the task resource allocation scheme corresponding to the updated individual sequentially performs resource occupation amount expansion matching, resource occupation conflict resolution and resource occupation amount reduction according to the preset task resource demand data. And calculating the fitness value of the updated individual according to a preset optimization objective function. The fitness value comprises a task planning failure rate of an individual corresponding task resource allocation scheme and a ground antenna load balance value. And acquiring an optimized individual from the updated individual according to the preset task resource allocation preference data and the individual fitness value. The task resource allocation preference data includes: a mission planning failure rate preference value and a ground antenna load balance preference value.

Specific limitations regarding the preferred MOEA-based satellite ground station resource multi-objective optimization apparatus may be found in the above limitations on the preferred MOEA-based satellite ground station resource multi-objective optimization method, and will not be described in detail herein. The various modules in the above-described preferred MOEA-based satellite ground station resource multi-objective optimization apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program when executed by a processor implements a method for optimizing satellite ground station resources based on a preference MOEA. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.

It will be appreciated by those skilled in the art that the structure shown in FIG. 7 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.

In one embodiment, a computer device is provided comprising a memory storing a computer program and a processor that when executing the computer program performs the steps of:

Updating a current individual based on a multi-target evolutionary algorithm, modifying the updated individual based on a heuristic strategy, sequentially carrying out resource occupation amount expansion matching, resource occupation conflict resolution and resource occupation amount reduction on a task resource allocation scheme corresponding to the updated individual, and acquiring an optimized individual from the updated individual according to preset task resource allocation preference data.

In one embodiment, the processor when executing the computer program further performs the steps of: a set of visible time windows of a ground antenna of a satellite ground station for a mission satellite is obtained. And constructing an individual corresponding to the task resource allocation scheme by using a preset coding rule according to the occupied state of the visible time window.

In one embodiment, the processor when executing the computer program further performs the steps of: the current individual is updated based on the multi-objective evolutionary algorithm. And modifying the updated individual according to a preset task expansion strategy, and increasing the occupation quantity of the visible time windows of the task resource allocation scheme corresponding to the updated individual to obtain the task expansion individual. And according to a preset conflict resolution strategy, resolving the resource occupation conflict in the task resource allocation scheme corresponding to the task expansion individual to obtain a conflict resolution individual. The resource occupancy conflict includes: ground antenna occupancy conflicts and mission satellite occupancy conflicts. The occupied visible time window in the task resource allocation scheme corresponding to the conflict resolution individual is obtained, and the occupied visible time window is shortened according to preset task resource demand data and task reduction strategies, so that the task reduction individual is obtained. And acquiring an optimized individual from the task reduction individuals according to preset task resource allocation preference data.

In one embodiment, the processor when executing the computer program further performs the steps of: and acquiring the occupation quantity of the visible time windows corresponding to the task satellites in the task resource allocation scheme corresponding to the updated individuals. When the occupation quantity of the visible time windows corresponding to the task satellites is smaller than a preset value, the occupation quantity of the visible time windows of the task satellites in the task resource allocation scheme is increased according to a preset expansion coefficient, and a task expansion individual is obtained.

In one embodiment, the processor when executing the computer program further performs the steps of: the occupied visible time window corresponding to each ground antenna in the task resource allocation scheme corresponding to the task expansion individual is obtained, and the ground antenna resource occupation conflict is resolved according to the preset antenna task duration and antenna task period conflict resolution rules. The occupied visible time window corresponding to each task satellite in the task resource allocation scheme after the ground antenna resource occupation conflict is resolved is obtained, task satellite resource occupation conflict is resolved according to the preset satellite task duration time and satellite task time period conflict resolution rules, and a conflict resolution individual is obtained.

In one embodiment, the processor when executing the computer program further performs the steps of: and acquiring an occupied visible time window corresponding to the ground antenna in the task resource allocation scheme corresponding to the conflict resolution individual, and reserving the time period of the occupied visible time window corresponding to the ground antenna within a preset task time period to obtain the time period reduction individual. The method comprises the steps of obtaining occupied visible time windows corresponding to task satellites in a task resource allocation scheme corresponding to a time period reduction individual, canceling the occupied visible time windows according to preset task satellite demand times, enabling the occupied number of the visible time windows corresponding to the task satellites to be equal to the task satellite demand times, and obtaining the task reduction individual.

In one embodiment, the processor when executing the computer program further performs the steps of:

The current individual is updated based on the multi-objective evolutionary algorithm. And modifying the updated individual based on the heuristic strategy, so that the task resource allocation scheme corresponding to the updated individual sequentially performs resource occupation amount expansion matching, resource occupation conflict resolution and resource occupation amount reduction according to the preset task resource demand data. And calculating the fitness value of the updated individual according to a preset optimization objective function. The fitness value comprises a task planning failure rate of an individual corresponding task resource allocation scheme and a ground antenna load balance value. And acquiring an optimized individual from the updated individual according to the preset task resource allocation preference data and the individual fitness value. The task resource allocation preference data includes: a mission planning failure rate preference value and a ground antenna load balance preference value.

In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:

In one embodiment, the computer program when executed by the processor further performs the steps of: a set of visible time windows of a ground antenna of a satellite ground station for a mission satellite is obtained. And constructing an individual corresponding to the task resource allocation scheme by using a preset coding rule according to the occupied state of the visible time window.

In one embodiment, the computer program when executed by the processor further performs the steps of: the current individual is updated based on the multi-objective evolutionary algorithm. And modifying the updated individual according to a preset task expansion strategy, and increasing the occupation quantity of the visible time windows of the task resource allocation scheme corresponding to the updated individual to obtain the task expansion individual. And according to a preset conflict resolution strategy, resolving the resource occupation conflict in the task resource allocation scheme corresponding to the task expansion individual to obtain a conflict resolution individual. The resource occupancy conflict includes: ground antenna occupancy conflicts and mission satellite occupancy conflicts. The occupied visible time window in the task resource allocation scheme corresponding to the conflict resolution individual is obtained, and the occupied visible time window is shortened according to preset task resource demand data and task reduction strategies, so that the task reduction individual is obtained. And acquiring an optimized individual from the task reduction individuals according to preset task resource allocation preference data.

In one embodiment, the computer program when executed by the processor further performs the steps of: and acquiring the occupation quantity of the visible time windows corresponding to the task satellites in the task resource allocation scheme corresponding to the updated individuals. When the occupation quantity of the visible time windows corresponding to the task satellites is smaller than a preset value, the occupation quantity of the visible time windows of the task satellites in the task resource allocation scheme is increased according to a preset expansion coefficient, and a task expansion individual is obtained.

In one embodiment, the computer program when executed by the processor further performs the steps of: the occupied visible time window corresponding to each ground antenna in the task resource allocation scheme corresponding to the task expansion individual is obtained, and the ground antenna resource occupation conflict is resolved according to the preset antenna task duration and antenna task period conflict resolution rules. The occupied visible time window corresponding to each task satellite in the task resource allocation scheme after the ground antenna resource occupation conflict is resolved is obtained, task satellite resource occupation conflict is resolved according to the preset satellite task duration time and satellite task time period conflict resolution rules, and a conflict resolution individual is obtained.

In one embodiment, the computer program when executed by the processor further performs the steps of: and acquiring an occupied visible time window corresponding to the ground antenna in the task resource allocation scheme corresponding to the conflict resolution individual, and reserving the time period of the occupied visible time window corresponding to the ground antenna within a preset task time period to obtain the time period reduction individual. The method comprises the steps of obtaining occupied visible time windows corresponding to task satellites in a task resource allocation scheme corresponding to a time period reduction individual, canceling the occupied visible time windows according to preset task satellite demand times, enabling the occupied number of the visible time windows corresponding to the task satellites to be equal to the task satellite demand times, and obtaining the task reduction individual.

In one embodiment, the computer program when executed by the processor further performs the steps of: the current individual is updated based on the multi-objective evolutionary algorithm. And modifying the updated individual based on the heuristic strategy, so that the task resource allocation scheme corresponding to the updated individual sequentially performs resource occupation amount expansion matching, resource occupation conflict resolution and resource occupation amount reduction according to the preset task resource demand data. And calculating the fitness value of the updated individual according to a preset optimization objective function. The fitness value comprises a task planning failure rate of an individual corresponding task resource allocation scheme and a ground antenna load balance value. And acquiring an optimized individual from the updated individual according to the preset task resource allocation preference data and the individual fitness value. The task resource allocation preference data includes: a mission planning failure rate preference value and a ground antenna load balance preference value.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.

Claims

1. A method for optimizing satellite ground station resources multi-objective based on preference MOEA, the method comprising:

acquiring an available resource set of a satellite ground station to a task satellite, and constructing an individual corresponding to a task resource allocation scheme according to a preset coding rule and the available resource set;

Updating a current individual based on a multi-objective evolutionary algorithm, modifying the updated individual based on a heuristic strategy, enabling a task resource allocation scheme corresponding to the updated individual to sequentially perform resource occupation amount expansion matching, resource occupation conflict resolution and resource occupation amount reduction according to preset task resource demand data, and acquiring an optimized individual from the updated individual according to preset task resource allocation preference data;

updating the current individual based on a multi-objective evolutionary algorithm;

modifying the updated individual according to a preset task expansion strategy, and increasing the occupation quantity of visible time windows of a task resource allocation scheme corresponding to the updated individual according to preset task resource demand data to obtain a task expansion individual;

Resolving resource occupation conflicts in a task resource allocation scheme corresponding to the task expansion individuals according to a preset conflict resolution strategy and preset task resource demand data to obtain conflict resolution individuals; the resource occupancy conflict includes: ground antenna occupation conflicts and mission satellite occupation conflicts;

The occupied visible time window in the task resource allocation scheme corresponding to the conflict resolution individual is obtained, and the occupied visible time window is shortened according to preset task resource demand data and a task reduction strategy, so that a task reduction individual is obtained;

acquiring an optimized individual from the task reduction individuals according to preset task resource allocation preference data;

2. The method of claim 1, wherein the step of obtaining a set of available resources of the satellite ground station for the mission satellite, and constructing an individual corresponding to the mission resource allocation scheme according to a preset encoding rule and the set of available resources comprises:

acquiring a set of visible time windows of a ground antenna of a satellite ground station to a task satellite;

3. The method according to claim 2, wherein the step of modifying the updated individual according to the preset task expansion policy and increasing the occupation number of the visible time window of the task resource allocation scheme corresponding to the updated individual according to the preset task resource demand data, to obtain the task expansion individual includes:

acquiring the occupation quantity of the visible time windows corresponding to the task satellites in the task resource allocation scheme corresponding to the updated individuals;

And when the occupation quantity of the visible time windows corresponding to the task satellites is smaller than a preset value, increasing the occupation quantity of the visible time windows of the task satellites in a task resource allocation scheme according to a preset expansion coefficient to obtain a task expansion individual.

4. The method according to claim 2, wherein the step of resolving the resource occupation conflict in the task resource allocation scheme corresponding to the task expansion individual according to the preset conflict resolution policy and the preset task resource demand data, to obtain the conflict resolution individual includes:

the occupied visible time window corresponding to each ground antenna in the task resource allocation scheme corresponding to the task expansion individual is obtained, and the ground antenna resource occupation conflict is resolved according to a preset antenna task duration and antenna task duration conflict resolution rule;

the occupied visible time window corresponding to each task satellite in the task resource allocation scheme after the ground antenna resource occupation conflict is resolved is obtained, and task satellite resource occupation conflict is resolved according to a preset satellite task duration time period and a satellite task time period conflict resolution rule, so that a conflict resolution individual is obtained.

5. The method of claim 2, wherein the step of obtaining the occupied visible time window in the task resource allocation scheme corresponding to the conflict resolution individual and shortening the occupied visible time window according to preset task resource demand data and task reduction policies, comprises the steps of:

Acquiring the occupied visible time window corresponding to the ground antenna in the task resource allocation scheme corresponding to the conflict resolution individual, and reserving the time period of the occupied visible time window corresponding to the ground antenna within a preset task time period to obtain a time period reduction individual;

The occupied visible time windows corresponding to the task satellites in the task resource allocation scheme corresponding to the time period reduction individuals are obtained, the occupied visible time windows are canceled according to the preset task satellite demand times, the number of the occupied visible time windows corresponding to the task satellites is enabled to be equal to the task satellite demand times, and the task reduction individuals are obtained.

6. The method according to any one of claims 1 to 5, wherein the step of updating the current individual based on the multi-objective evolutionary algorithm, modifying the updated individual based on the heuristic strategy, and sequentially performing resource occupation amount expansion matching, resource occupation conflict resolution and resource occupation amount reduction on the task resource allocation scheme corresponding to the updated individual according to the preset task resource demand data, and obtaining the optimized individual from the updated individual according to the preset task resource allocation preference data comprises:

Modifying the updated individual based on the heuristic strategy, so that a task resource allocation scheme corresponding to the updated individual sequentially performs resource occupation amount expansion matching, resource occupation conflict resolution and resource occupation amount reduction according to preset task resource demand data;

Calculating an updated fitness value of the individual according to a preset optimization objective function; the adaptability value comprises a task planning failure rate of a task resource allocation scheme corresponding to an individual and a ground antenna load balance value;

acquiring an optimized individual from the updated individual according to preset task resource allocation preference data and individual fitness values; the task resource allocation preference data includes: a mission planning failure rate preference value and a ground antenna load balance preference value.

7. A satellite ground station resource multi-objective optimization apparatus based on preference MOEA, the apparatus comprising:

The individual construction module is used for acquiring an available resource set of the satellite ground station to the task satellite and constructing an individual corresponding to the task resource allocation scheme according to a preset coding rule and the available resource set;

The individual iteration updating module is used for updating the current individual based on the multi-objective evolutionary algorithm, modifying the updated individual based on the heuristic strategy, enabling a task resource allocation scheme corresponding to the updated individual to sequentially perform resource occupation amount expansion matching, resource occupation conflict resolution and resource occupation amount reduction according to preset task resource demand data, and acquiring an optimized individual from the updated individual according to preset task resource allocation preference data; updating the current individual based on a multi-objective evolutionary algorithm; modifying the updated individual according to a preset task expansion strategy, and increasing the occupation quantity of visible time windows of a task resource allocation scheme corresponding to the updated individual according to preset task resource demand data to obtain a task expansion individual; resolving resource occupation conflicts in a task resource allocation scheme corresponding to the task expansion individuals according to a preset conflict resolution strategy and preset task resource demand data to obtain conflict resolution individuals; the resource occupancy conflict includes: ground antenna occupation conflicts and mission satellite occupation conflicts; the occupied visible time window in the task resource allocation scheme corresponding to the conflict resolution individual is obtained, and the occupied visible time window is shortened according to preset task resource demand data and a task reduction strategy, so that a task reduction individual is obtained; acquiring an optimized individual from the task reduction individuals according to preset task resource allocation preference data;

And the task resource allocation scheme output module is used for obtaining the optimized task resource allocation scheme of the satellite ground station according to the optimized individual when the preset iteration update termination condition is met.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.

9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 6.